More than ever, artificial intelligence is a part of our environment. AI has impacted our lives and is now present in at least some of the things we do on a daily basis, from our homes' Alexa and Cortana telling us the time to digital twins solving the issue of supply chains and websites suggesting things to buy based on our buying habits. Applying for jobs is a significant activity where AI is prevalent. AI-based recruiting is more of an effort on the part of businesses to find talent in "better" ways. As a result of the pandemic's impact on and outright disruption of the future of work, more people are choosing to work virtually or from home, increasing the allure of applying online. The eyes of a human recruiter can only be so precise when there are thousands of resumes pouring in for a single position at a company. Enter artificial intelligence (AI) to assist in identifying the targeted experience (as that is typically the yardstick by which businesses judge candidates) as well as the knowledge and skills of applicants from their resumes. The analysis, sorting, and delivery of resumes that matched the employment experience and talents the organization sought to succeed in the post was then handled by AI, taking over the menial, repetitive chores.

According to theory, as AI progresses from hiring to supporting candidate tasks like sourcing, selection, onboarding, and even terminations, businesses will have more opportunities to hire employees more cheaply and effectively, shifting HR brainpower to more value-driven work and decision-making. However, we must always keep in mind that artificial intelligence (AI) is still a tool created by humans that will make decisions and think similarly to us, sometimes even more rigorously. From there, bias against some applicants may start and grow, preventing them from being given a fair shot at employment. I make this point specifically in terms of experience (if you only have 4.25 years instead of the full 5, would that place your resume in the reject pile?). However, human demographics such as ability, color, age, and gender are more concerning in terms of bias. Ai is built from resources including training data and machine learning and both of those are built to understand human trends. It’s all but a solid guarantee that those resources used to build AI have some form of human bias if the company using it is trying to achieve a hiring goal for its roles. Companies must possess a specific attribute or characteristic that will lead to some exclusionary employment practices since they are driven to the clear economic purpose of growing revenue and minimizing costs.

Given the existence of human bias, AI should be used very little in the recruiting process, providing simple information and refraining from making any judgment-based comments or pronouncements. The focus should shift toward organizational development and organizational change for the professionals already employed by the company given our ability to gather information and interpret it. Employee experience by assisting with employee benefits or team collaboration, employee engagement by deploying chatbots to perform wellness checks throughout the day, and direct professional development by launching training, learning, and even coaching to the point where it can guide the authentic and value-driven "5w-1h" - who needs training, what subjective or objective goal they need it on when they would need it.

When to engage the employee is one way AI could improve training and learning. AI could use the four types of analytics to analyze the performance of a job function and suggest that workers enroll in courses or training to prepare them for management or leadership positions. In order to reach or exceed the requirements set by the firm for that role, one example would be to use predictive analytics to foresee how the professional's relationship-building skills could effect leadership in that position. Another example would be AI developing a leadership or other career curriculum for a set of employees who exhibit specific traits by employing diagnostic analytics to determine the corporate cultural habits of its personnel. From there, the business may design its workforce positions to create "win-win" scenarios in which people receive the career prospects they desire in the form of higher compensation and other career benefits and businesses enhance employee retention and build a leadership pipeline for the future. Still another, and probability the most common at immediate thought, would be descriptive analytics in reporting patterns in role or talent performance and productivity to not only give company leadership a glimpse of work patterns that are or are not working but could recommend remedies or interventions from appreciative inquiry (build on strength or opportunistic patterns) and organizational change (improve on weakness or threatening patterns) that would help companies allocate resources accordingly.

AI's impact on or awareness of human bias is not unaffected by its presence in employee learning, coaching, training, or organizational development or transformation. Keep in mind that businesses might contribute the developments they believe would help their employees succeed in the market where they do business. The corporation can request that training be in sales development and enter it into AI to painstakingly study and report on patterns for experts in the sales department. A salesperson may exceed quota the first two weeks and perform well, but the following two weeks may show a pattern of poor performance. When the professional does not require it, AI would then report this and propose that they attend sales development training because of this tendency. Another instance could be the selection of leaders. The aforementioned demographics could be lingering with AI making a decision of providing recommendations for leadership courses to a group that may be within a specific age range consistently (again, due to positive results of performance) and creates discrimination as it could not tell the difference or make a reporting decision based on age.

For staff growth, AI should be a complimentary partner in giving knowledge and recommendation across a fair board. We should be conscious of the fact that it, like us, does not always produce accurate results when we use it. Although I believe that using it for learning and training would be much more important to a company's success than using it to locate and employ people, it should still not be used as a criterion for professional advancement. The use of AI in professional development would also be a great and quick step toward investing in staff members and have the potential to allow the C-suite to engage with its workers through education and training. people should always lead learning and training for other people, while yet allowing AI to play a fixed role. When it comes to that job, AI can be the sidekick and even the heroic companion, but it must never be the hero of professional progress.